Evaluation of EMG, force and joystick as control interfaces for active arm supports

Background:\ud The performance capabilities and limitations of control interfaces for the operation of active movement-assistive devices remain unclear. Selecting an optimal interface for an application requires a thorough understanding of the performance of multiple control interfaces. \ud \ud Methods:\ud In this study the performance of EMG-, force- and joystick-based control interfaces were assessed in healthy volunteers with a screen-based one-dimensional position-tracking task. The participants had to track a target that was moving according to a multisine signal with a bandwidth of 3 Hz. The velocity of the cursor was proportional to the interface signal. The performance of the control interfaces were evaluated in terms of tracking error, gain margin crossover frequency, information transmission rate and effort. \ud \ud Results:\ud None of the evaluated interfaces was superior in all four performance descriptors. The EMG-based interface was superior in tracking error and gain margin crossover frequency compared to the force- and the joystick-based interfaces. The force-based interface provided higher information transmission rate and lower effort than the EMG-based interface. The joystick-based interface did not present any significant difference with the force-based interface for any of the four performance descriptors. We found that significant differences in terms of tracking error and information transmission rate were present beyond 0.9 and 1.4 Hz respectively. \ud \ud Conclusions:\ud Despite the fact that the EMG-based interface is far from the natural way of interacting with the environment, while the force-based interface is closer, the EMG-based interface presented very similar and for some descriptors even a better performance than the force-based interface for frequencies below 1.4 Hz. The classical joystick presented a similar performance to the force-based interface and holds the advantage of being a well established interface for the control of many assistive devices. From these findings we concluded that all the control interfaces considered in this study can be regarded as a candidate interface for the control of an active arm support

Using position dependent damping forces around reaching targets for transporting heavy objects:A Fitts' law approach

Passive assistive devices that compensate gravity can reduce human effort during transportation of heavy objects. The additional reduction of inertial forces, which are still present during deceleration when using gravity compensation, could further increase movement performance in terms of accuracy and duration. This study investigated whether position dependent damping forces (PDD) around targets could assist during planar reaching movements. The PDD damping coefficient value increased linearly from 0 Ns/m to 200 Ns/m over 18 cm (long PDD) or 9 cm (short PDD). Movement performance of reaching with both PDDs was compared against damping free baseline conditions and against constant damping (40 Ns/m). Using a Fitts' like experiment design 18 subjects performed a series of reaching movements with index of difficulty: 3.5, 4.5 and 5.5 bits, and distances 18, 23 and 28 cm for all conditions. Results show that PDD reduced (compared to baseline and constant damping) movement times by more than 30% and reduced the number of target reentries, i.e. increasing reaching accuracy, by a factor of 4. Results were inconclusive about whether the long or short PDD conditions achieved better task performance, although mean human acceleration forces were higher for the short PDD, hinting at marginally faster movements. Overall, PDD is a useful haptic force to get humans to decrease their reaching movement times while increasing their targeting accuracy

Resistance is Not Futile: Haptic Damping Forces Mitigate Effects of Motor Noise during Reaching

Understanding how users adapt their motor behavior to damping forces can improve assistive haptic shared control strategies, for instance in heavy robot-assisted lifting applications. In previous experiments we showed that subjects reaching in constant and position-dependent longitudinal damping fields were able to reduce their movement time and increase end-point accuracy. The movement time versus movement distance and prescribed end-point accuracy agreed with Fitts' Law. However, why subjects were able to have shorter movement time while subjected to impeding damping forces is not explained by Fitts' Law. Based on the minimal variance principle we propose that humans exploit the noise-filtering behavior of constant or position-dependent damping forces. These damping forces attenuate mechanical effects of activation-dependent motor noise. This allows for higher motor activation and shorter movement time without losing end-point accuracy. Consequently, higher allowed motor activation allows for higher accelerations that lead to higher peak velocities, resulting in shorter movement times. Linear and non-linear stochastic optimal feedback control and optimal estimation models with multiplicative noise corroborate measurement data, supporting our hypothesis

Adaptive gravity and joint stiffness compensation methods for force-controlled arm supports

People with muscular weakness can benefit from arm supports that compensate the weight of their arms. Due to the disuse of the arms, passive joint stiffness increases and providing only gravity compensation becomes insufficient to support the arm function. Hence, joint stiffness compensation is also required, for which the use of active arm supports is essential. Force-based control interfaces are a solution for the operation of arm supports. A critical aspect of force-based interfaces, to properly detect the movement intention of the user, is the ability to distinguish the voluntary forces from any other force, such as gravity or joint stiffness forces. Model- and calibration-based strategies for the estimation of gravity and joint stiffness forces lack adaptability and are time consuming since they are measurement dependent. We propose two simple, effective and adaptive methods for the compensation of forces resulting from gravity and joint stiffness. The compensation methods are based on the estimation of the ompensation force using a low-pass filter, and switching of control parameters using a finite state machine. The compensation methods were evaluated with an adult man suffering from Duchenne muscular dystrophy with very limited arm function. The results show that when gravity and joint stiffness forces were adaptively compensated the reachable workspace of the user was increased more than 50% compared to the workspace reached when only constant gravity compensation was provided

Switching proportional EMG control of a 3D endpoint arm support for people with duchenne muscular dystrophy

Duchenne muscular dystrophy (DMD) is a disease resulting in progressive muscle degeneration. Active arm supports can improve the quality of life for people with DMD by augmenting the residual motor capabilities of their arm. As an extension of our previous study, this research aims at developing a EMG-based control interface to detect the user's movement intention required to control more than 1-DOF. The interface switches between two horizontal and one vertical translations. Translations are proportionally controlled by EMG. The passive interaction torques measured between the arm and the active arm support, are used to make the robot's endpoint resemble a gimbal mechanism. Hence decreasing the endpoint's DOF from six to three by actively reducing the impedance of the rotational DOF. A preliminary evaluation of the control method has been carried out with one healthy subject, within a series of 2-D horizontal tracing and 3-D horizontal-vertical reaching tasks. A pilot study was also conducted with a boy with DMD controlling the device in a 2-D horizontal tracing task. Performance was evaluated in terms of path efficiency, smoothness, task completion rate and time. The results indicate that the control method is able to successfully detect the intention of the user and translate it into the intended movement. Furthermore, the reduction of the endpoint's DOF, results in a simple yet functional controller able to support natural movements of the arm

Towards Intuitive Control of Active Arm Supports for Men with Duchenne Muscular Dystrophy:A Hybrid sEMG-Torque Control Interface

Duchenne muscular dystrophy (DMD) is caused by a recessive X-linked mutation, resulting in progressive muscle degeneration and eventual death. While the lifespan of men with DMD increased due to improvements on health care, they still live with severe physical impairments and strong dependency on care. Active arm supports can improve their quality of life, by augmenting their arm’s residual motor capabilities. This research aims to develop a control interface, which detects the user’s movement intention, required to operate these active arm supports. We propose a hybrid sEMG-torque interface controlling three translations and two rotations of the forearm. The translations are proportionally controlled using the envelopes of sEMG signals from six arm muscles. The two horizontal translations can be controlled simultaneously while the vertical translation is sequentially controlled triggering a sEMG-based switch. The passive interaction torques measured between the forearm and the active arm support, are used to release two rotations of the forearm, mimicking a gimbal mechanism. A preliminary evaluation of the control method has been carried out with one healthy subject, within a series of two-dimensional and three-dimensional tasks. The performance was evaluated, in terms of path efficiency, task completion rate and time, smoothness, and overshoot. The results indicate that the control method is able to successfully detect the intention of the user and translate it to the intended movement. Furthermore, the combination of sEMG and torque control of the forearm translations and rotations, results in a simple, yet functional controller able to support natural movements of the arm

Implementation of EMG- and Force-Based Control Interfaces in Active Elbow Supports for Men With Duchenne Muscular Dystrophy: A Feasibility Study

While there is an extensive number of studies on the development and evaluation of electromyography (EMG)- and force-based control interfaces for assistive devices, no studies have focused on testing these control strategies for the specific case of adults with Duchenne muscular dystrophy (DMD). This paper presents a feasibility study on the use of EMG and force as control interfaces for the operation of active arm supports for men with DMD. We have built an experimental active elbow support, with a threefold objective: 1) to investigate whether adult men with DMD could use EMG- and force-based control interfaces; 2) to evaluate their performance during a discrete position-tracking task; and 3) to examine users' acceptance of the control methods. The system was tested in three adults with DMD (21-22 years). Although none of the three participants had performed any voluntary movements with their arms for the past 3-5 years, all of them were 100% successful in performing the series of tracking tasks using both control interfaces (mean task completion time EMG: 6.8 ± 4.8 s, force: 5.1 ± 1.8 s). While movements with the force-based control were considerably smoother in Subject 3 and faster in Subject 1, EMG based-control was perceived as less fatiguing by all three subjects. Both EMG- and force-based interfaces are feasible solutions for the control of active elbow supports in adults with DMD and should be considered for further investigations on multi-DOF control